This paper presents an approach to large lexicon sign recognition that does not require tracking. This overcomes the issues of how to accurately track the hands through self occlusion in unconstrained video, instead opting to take a detection strategy, where patterns of motion are identified. It is demonstrated that detection can be achieved with only minor loss of accuracy compared to a perfectly tracked sequence using coloured gloves. The approach uses two levels of classification. In the first, a set of viseme classifiers detects the presence of sub-Sign units of activity. The second level then assembles visemes into word level Sign using Markov chains. The system is able to cope with a large lexicon and is more expandable than traditional word level approaches. Using as few as 5 training examples the proposed system has classification rates as high as 74.3\% on a randomly selected 164 sign vocabulary performing at a comparable level to other tracking based systems.

@INPROCEEDINGS{Cooper_Large_2007,
author = {Helen Cooper and Richard Bowden},
title = {Large Lexicon Detection Of Sign Language},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision
Workshop : Human Computer Interaction},
year = {2007},
pages = {88 -- 97},
address = {Rio de Janario, Brazil},
month = oct # { 16 -- 19},
abstract = {This paper presents an approach to large lexicon sign recognition
that does not require tracking. This overcomes the issues of how
to accurately track the hands through self occlusion in unconstrained
video, instead opting to take a detection strategy, where patterns
of motion are identified. It is demonstrated that detection can be
achieved with only minor loss of accuracy compared to a perfectly
tracked sequence using coloured gloves. The approach uses two levels
of classification. In the first, a set of viseme classifiers detects
the presence of sub-Sign units of activity. The second level then
assembles visemes into word level Sign using Markov chains. The system
is able to cope with a large lexicon and is more expandable than
traditional word level approaches. Using as few as 5 training examples
the proposed system has classification rates as high as 74.3\% on
a randomly selected 164 sign vocabulary performing at a comparable
level to other tracking based systems.},
doi = {10.1007/978-3-540-75773-3_10},
url = {http://personal.ee.surrey.ac.uk/Personal/H.Cooper/research/papers/LargeLexiconDetection.pdf}
}